This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. We propose a general mesh-based atlas representation, and compare diff...
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundle...
Mahnaz Maddah, Andrea J. U. Mewes, Steven Haker, W...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
In this paper, we propose an efficient mechanism dealing with trust assessment for agent societies, aiming to accurately assess the trustworthiness of the collaborating agents. I...